Wednesday, August 2, 2017

Neuroscience inspired Computer Vision


Having read the profound master piece “When breath becomes air”, by Neuroscientist – surgeon Paul Kalanithi, I was curious about how neuroscience could contribute to AI (Computer vision in particular). 

Then, I found an comprehensive article in Neuron Review journal (written by Demis Hassabis, Dharshan Kumaran, Christopher Summerfield, Matthew Botvinick) titled “Neuroscience inspired Artificial Intelligence”.  Here goes a brief excerpt of concepts I found inspiring in that article, related to computer vision.

  • How visual input is filtered and pooled into simple and complex areas of cells in area V1in visual cortex
  • Hierarchical organization of mammalian cortical systems 
Object recognition 
  • Transforming raw visual input into increasingly complex set of features - To achieve invariance towards pose, illumination and scale
  • Visual attention shifts strategically among different objects (no equal priority for all objects) - To ignore irrelevant objects in a given scene in the presence of a clutter, multi object recognition, image to caption generation, generative models to synthasize images 
Intuitive understanding of physical world 
  • Interpret and reason about scenes by decomposing them into individual objects and their relations 
  • Redundency reduction (encourages the emergence of disentangled representations of independent factors such as shape and position) - To learn objectness, construct rich object models from raw inputs using deep generative models, E.g., Variational auto encoder 
Efficient Learning 
  • Rapidly learn new concepts from only a handful of examples (Related with Animal learning, developmental psychology) 
  • Characters challenge - distinguish novel instances of an unfamiliar hand written character from another - "Learn to learn”  networks
Transfer Learning
  • Generalizing or transferring generalized knowledge gained in one context to novel previously unseen domains (E.g., Human who can drive a car drives an unfamiliar vehicle) - Progressive networks 
  • Neural coding using Grid codes in Mammalian entorhinal cortex - To formulate conceptual representations that code abstract, relational information among patterns of inputs (not just invariant features) 
Virtual brain analytics 
  • Increase the interpretability of AI computations, Determine response properties of units in a neural networks 
  • Activity maximization - To generate synthetic images by maximizing the activity of certain classes of unit 
From AI to neuroscience
  • Enhancing performances of CNNs has also yielded new insights into the nature of neural representations in high-level visual areas. E.g., 30 network architectures from AI to explain the structure of the neural representations observed in the ventral visual stream of humans and monkeys